Various global or local communications networks (the Internet, the World Wide Web, local area networks and the like) offer a user a vast amount of information. Generally speaking, a given user can access a web resource via a communication network by two principle means. The given user can access a particular resource directly, either by typing an address of the resource (typically an URL or Universal Resource Locator, such as www.webpage.com) or by clicking a link in an e-mail or in another web resource. Alternatively, the given user may conduct a search using a search engine to locate a resource of interest. The latter is particularly suitable in those circumstances, where the given user knows a topic of interest, but does not know the exact address of the resource she is interested in.
With the growth of the number of users accessing the Internet, as well as the growth of the number of web resources available in the Internet, content recommendation systems have emerged for providing content recommendations to the users. These applications automate the process of providing personalized recommendations for content, other web resources, and services that might be of interest to the user.
However, the overwhelming volume of targeted content items available, as well as the scarce information about the user, make it difficult for the content recommendation application to choose which targeted content item the user would be more interested in.
Generally speaking, there exist several computer-based approaches for implementing the targeted content item delivery application. A common approach is to associate a user with a group other users who share the same interests as the user and then recommending a targeted content item enjoyed by others in the group. This approach can be broadly classified as “look alikes”, which is based on a fundamental premise that users having similar characteristics are likely to enjoy similar content. Such an approach would collect the activity history for a plurality of users and train a singular value decomposition (SVD) algorithm used to associate a user profile to a group.
While such an approach may be useful, it also has several downsides. For example, a large amount of activity history is required for building reliable profiles, and as a result, training the SVD algorithm can be very expensive in terms of computing resources.
US 2016/343056 (published on Nov. 24, 2016) discloses a computer-implemented method for recommending items for future purchase by a consumer based on the consumer's historical actions is provided. The method is implemented using an adaptive recommendation (“AR”) computer device in communication with a memory. The method includes receiving from a recommender server computing device at least one preference vector, storing the at least one preference vector, receiving at least one consumer action including a time aspect, from a candidate consumer, and determining at least one personalized vector based on the at least one preference vector, the at least one consumer action, and the time aspect associated with each of the at least one consumer action. The at least one personalized vector represents at least one future purchase that the candidate consumer will likely conduct. The method also includes displaying at least one recommendation to the candidate consumer based on the at least one personalized vector.
U.S. Pat. No. 6,134,532 (published on Oct. 17, 2000) discloses a system and method for selecting and presenting personally targeted entities such as advertising, coupons, products and information content, based on tracking observed behavior on a user-by-user basis and utilizing an adaptive vector space representation for both information and behavior. The system matches users to entities in a manner that improves with increased operation and observation of user behavior. User behavior and entities (ads, coupons, products) and information (text) are all represented as content vectors in a unified vector space. The system is based on an information representation called content vectors that utilizes a constrained self organization learning technique to learn the relationships between symbols (typically words in unstructured text). Users and entities are each represented as content vectors.